Visualizing video audience retention by impression frequency

ABSTRACT

Systems and methods for visualizing audience retention for a video as a function of impression frequency are provided. In an aspect, a system includes a reception component configured to receive watch history information for a plurality of users regarding their watch history of a video, and a visualization component configured to generate a visualization based on the watch history information that graphically depicts viewer retention over duration of the video as a function of impression frequency.

TECHNICAL FIELD

This application generally relates to systems and methods forvisualizing video audience retention as a function of impressionfrequency.

BACKGROUND

The proliferation of available streaming content is increasing atexponential levels that will soon reach many millions if not billions ofavailable streaming content for viewing. Conventionally, broadcast mediahas been provided by television or cable channels that are typicallyprovided by a relatively small number of content providers. However,with the ubiquitous nature of media creation and publishing tools,individuals are able to become prolific content creators. This hasresulted in the exponential growth of available streaming content aswell as available channels for streaming content. As a result, videocreators are constantly searching for ways to attract and retainviewers. Understanding how viewers perceive and consume their videos isessential for discovering new and improved ways to achieve this goal.

BRIEF DESCRIPTION OF THE DRAWINGS

Numerous aspects, embodiments, objects and advantages of the presentinvention will be apparent upon consideration of the following detaileddescription, taken in conjunction with the accompanying drawings, inwhich like reference characters refer to like parts throughout, and inwhich:

FIG. 1 illustrates an example system for visualizing video audienceretention as a function of impression frequency in accordance withvarious aspects and embodiments described herein;

FIG. 2 presents an example visualization that charts audience retentionas a function of impression frequency for a video in accordance withvarious aspects and embodiments described herein;

FIG. 3 presents another example visualization that charts audienceretention as a function of impression frequency for a video inaccordance with various aspects and embodiments described herein;

FIG. 4 presents another example visualization that charts audienceretention as a function of impression frequency for a video inaccordance with various aspects and embodiments described herein;

FIG. 5 illustrates another example system for visualizing video audienceretention as a function of impression frequency in accordance withvarious aspects and embodiments described herein;

FIG. 6 presents example comparison visualizations that chart audienceretention as a function of impression frequency for a video with respectto different audience subsets in accordance with various aspects andembodiments described herein;

FIG. 7 illustrates another example system for visualizing video audienceretention as a function of impression frequency in accordance withvarious aspects and embodiments described herein;

FIG. 8 present an example user interface that facilitates receivinginput regarding generation of a visualization that charts aspects ofwatch history data as a function of impression frequency in accordancewith various aspects and embodiments described herein;

FIG. 9 illustrates another example system for visualizing video audienceretention as a function of impression frequency in accordance withvarious aspects and embodiments described herein;

FIG. 10 is a flow diagram of an example method for visualizing videoaudience retention as a function of impression frequency in accordancewith various aspects and embodiments described herein;

FIG. 11 is a flow diagram of another example method for visualizingvideo audience retention as a function of impression frequency inaccordance with various aspects and embodiments described herein;

FIG. 12 is a schematic block diagram illustrating a suitable operatingenvironment in accordance with various aspects and embodiments.

FIG. 13 is a schematic block diagram of a sample-computing environmentin accordance with various aspects and embodiments.

DETAILED DESCRIPTION

The innovation is described with reference to the drawings, wherein likereference numerals are used to refer to like elements throughout. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofthis innovation. It may be evident, however, that the innovation can bepracticed without these specific details. In other instances, well-knownstructures and components are shown in block diagram form in order tofacilitate describing the innovation.

By way of introduction, the subject matter described in this disclosurerelates to systems and methods for visualizing audience retention as afunction of impression frequency. Audience or viewer retention refers tothe number of viewers a video retains over the duration of the videoplay. For example, at the onset of video play, 100% of the viewers arepresented the video, 75% of these viewers may make it through the first10% of the video, 50% of the viewers may make it through 40% of thevideo, 25% of the viewers may make it through 75% of the video and 10%of the viewers may make it through the entire video. Audience retentiondata provides useful insight regarding where certain types of users tendto stop watching a video, such as a video advertisement. Thisinformation is helpful to video advertisers wanting to improve audienceretention. For example, based on audience retention data for aparticular video advertisement, the video advertiser can improveplacement of the video advertisement with viewers that tend to watchgreater percentages of the video.

Often times, a video advertisement is presented to the same usermultiple times to gain viewer attention and affinity for a product basedon the mere-exposure effect. However, where viewers have the option toexit the advertisement prior to completion, repeated exposure of somevideo advertisements to certain users can be useless. For example, wherea user exited a video advertisement early the first time it was seen, itis unlikely that the same user will watch the video advertisement tocompletion when shown a second time. On the contrary, a videoadvertisement that is frequently re-watched to completion by many userswould likely benefit from being represented to these and other users ona regular basis.

Therefore, in addition to audience retention data, informationdescribing how many viewers continue watching a video as it is playedwhen the viewers have been exposed to the video once before, twicebefore, three times before, etc., is also useful to video advertiserswanting to improve overall audience retention. For example, whereaudience retention greatly declines after viewers have been presented avideo advertisement twice before, the video advertiser can set a maximumexposure threshold for exposure of the video advertisement to a sameuser. The number of times a single user has been presented or played avideo is referred to herein as impression frequency.

The subject disclosure provides a new way of processing and visualizingaudience retention data as a function of impression frequency. In anaspect, a visualization is generated that graphically depicts how userswatch a video over multiple impressions of the video. For example, agraph can be generated that charts percentage of users watching a videoat respective points throughout the progression of a video. The userscan be divided into different audience groups or user buckets, whereineach audience group or user buck includes users that have been presentedthe video different numbers of times (e.g., first audience groupincludes users that have been presented the video once, second audiencegroup includes users that have been presented the video twice, etc.).Each of the different audience groups or user buckets can bedistinguished from one another on the graph.

With the subject visualization techniques, a single graph orvisualization can present information that compares audience retentionfor a given video with respect to different audience impression groups.As a result, an advertiser can easily look at a visualization for avideo advertisement and understand that 30% of users who saw the videotwice made it to halfway through the video (e.g., 50% of the video wasplayed before the 30% of users stopped watching the video) as comparedto 60% of users who saw the video for the first time.

In one or more aspects, a system is provided that includes a systemincludes a reception component configured to receive watch historyinformation for a plurality of users regarding their watch history of avideo, and a visualization component configured to generate avisualization based on the watch history information that graphicallydepicts viewer retention over duration of the video as a function ofimpression frequency.

In another aspect, a method is disclosed that includes using a processorto execute the following computer executable instructions stored in amemory to perform the following acts: receiving watch historyinformation for a plurality of users regarding their watch history of avideo, and generating a visualization based on the watch historyinformation that graphically depicts viewer retention over duration ofthe video as a function of impression frequency.

Further provided is a tangible computer-readable storage mediumcomprising computer-readable instructions that, in response toexecution, cause a computing system to perform various operations. Theseoperations include identifying receiving information for a plurality ofusers regarding their watch history of a video, and generating avisualization based on the information that graphically depicts viewerretention over duration of the video as a function of impressionfrequency, wherein viewer retention over the duration of the videorefers to percentages of the plurality of users that have viewed thevideo up to respective points in the video, and wherein impressionfrequency relates to number of times the video has been presented torespective users of the plurality of users.

Referring now to the drawings, with reference initially to FIG. 1,presented is diagram of an example system 100 for visualizing audienceretention data as a function of impression frequency, in accordance withvarious aspects and embodiments described herein. Aspects of systems,apparatuses or processes explained in this disclosure can constitutemachine-executable components embodied within machine(s), e.g., embodiedin one or more computer readable mediums (or media) associated with oneor more machines. Such components, when executed by the one or moremachines, e.g., computer(s), computing device(s), virtual machine(s),etc. can cause the machine(s) to perform the operations described.

System 100 includes media provider 102, client device 116 and one ormore networks 114 for connecting media provider 102 and client device116. Media provider 102 can include an entity configured to providemedia (e.g., streaming video or audio) to a client device 116 via anetwork 114. Media provider 102 includes audience review platform 104 tofacilitate processing audience retention data and impression frequencydata related to video consumption at media provider 102. For example,audience review platform 104 can generate a visualization thatillustrates audience retention for a video advertisement as a functionof audience impression frequency. In an aspect, the visualization can bepresented to a user of client device 116 via a graphical user interface(GUI) generated at client device 116 via presentation component 118.

Generally, audience review platform 104 can include memory 112 thatstores computer executable components and processor 110 that executescomputer executable components stored in the memory, examples of whichcan be found with reference to FIG. 11. It is to be appreciated thatalthough audience review platform 104 is illustrated as being acomponent internal to media provider 102, such implementation is not solimited. For example, audience review platform 104 (and/or one or morecomponents of audience review platform 104) can be included in clientdevice 116, another content server, a cloud, and/or a media player.

Media provider 102 can include an entity that provides media content(e.g., video, streaming video, live streaming video, videoadvertisements, animations, images, thumbnails or other dynamic orstatic representations of video) to a client device 116 via a network114 (e.g., the Internet). Client device 116 can include presentationcomponent 118 to generate a user interface (e.g., a graphical userinterface or virtual interface) that displays media content provided bymedia provider 102 to a user of the client device. In an aspect,presentation component 118 can include an application (e.g., a webbrowser) for retrieving, presenting and traversing information resourceson the World Wide Web. For example, media provider 102 can provideand/or present media content to a client device 116 via a website thatcan be accessed using a browser of the client device 116. In anotherexample, media provider 102 can provide and/or present media content toa client device 116 via a cellular application platform. According tothis application, presentation component 118 can employ a clientapplication version of the media provider that 102 that can access thecellular application platform of media provider 102. In an aspect, themedia content can be presented and/or played at client device 116 usinga video player associated with media provider 102 and/or client device116.

Client device 116 can include any suitable computing device associatedwith a user and configured to interact with media provider 102, and/oraudience review platform 104. For example, client device 116 can includea desktop computer, a laptop computer, a television, an Internet enabledtelevision, a mobile phone, a smartphone, a tablet personal computer(PC), or a personal digital assistant PDA. As used in this disclosure,the terms “content consumer” or “user” refer to a person, entity,system, or combination thereof that employs system 100 (or additionalsystems described in this disclosure) using a client device 116.Network(s) 114 can include wired and wireless networks, including butnot limited to, a cellular network, a wide area network (WAD, e.g., theInternet), a local area network (LAN), or a personal area network (PAN).For example, client device 116 can communicate with media provider 102(and vice versa) using virtually any desired wired or wirelesstechnology, including, for example, cellular, WAN, wireless fidelity(Wi-Fi), Wi-Max, WLAN, and etc. In an aspect, one or more components ofsystem 100 are configured to interact via disparate networks.

In an embodiment, audience review platform 104 includes receptioncomponent 106 and visualization component 108. Reception component 106is configured to receive data from media provider 102 or an externalsystem/source (e.g., via a network 114) regarding a plurality of user'swatch history of a video. In particular, reception component 106 isconfigured to receive at least audience or viewer retention informationfor the video and viewer impression frequency information for the video.In an aspect, reception component 106 can extract audience or viewerretention information and viewer impression frequency information for avideo from watch histories of a plurality of users that have beenpresented the video.

As described above, audience or viewer retention information for a video(e.g., a video advertisement) includes information identifyingrespective amounts of the video watched by viewers after initiation ofplaying of the video for watching by the viewers. The video can includeany suitable type of video having any suitable duration. However,various aspects of the subject disclosure will be exemplified inassociation with analysis of audience retention and impression frequencydata associated with a video advertisement (e.g., a streaming videoadvertisement played before, during, or after the playing of anotherprimary video). In an aspect, audience retention information identifiesa percentage or number of users who were still watching a video at anygiven point in the video with respect to the total number of users whobegan watching the video. For example, audience retention informationfor a video can indicate that 37% of viewers that began watching thevideo remained at time marker 00:02:33 or that 60% of the viewers thatbegan watching the video made it past the 30 second time marker. Viewerretention information for a video can also identify respective points inthe video where viewers drop off or stop watching the video.

Viewer impression frequency information refers to number of timesrespective viewers of a video have been presented the video. Forexample, video advertisements are often played in association with asame or different video, to a same user multiple times. In an aspect,each time a user watches a video, the user's interaction and/or watchduration is noted (e.g., in a video log that records user interactionwith the video, in the user's watch history) along with informationindicating the number of times the user has been presented the video.

In an aspect, reception component 106 is configured to receive audienceretention information and viewer impression frequency information for avideo (e.g., from information logs associated with media provider 102such as user watch history logs, a video log that records userinteraction with the video, etc.). In another aspect, receptioncomponent 106 can receive and/or filter audience retention informationfor a video as a function of viewer impression frequency. In particular,reception component 106 can receive and/or filter information for avideo that identifies how different audience groups view a video,wherein the different audience groups are organized/filtered based onnumber of times the respective members of the group have been presentedthe video. For example, reception component 106 can receive and/orgenerate viewer retention information for a video that describespercentages of viewers that have viewed the video up to respectivepoints in the video when the viewers watched the video at firstimpression (e.g., the first time the viewers were presented the video).In addition, reception component 106 can receive and/or generate viewerretention information for the video that describes percentages ofviewers that have viewed the video up to respective points in the videowhen the viewers watched the video at second impression, thirdimpression, fourth impression, etc.

It should be appreciated that a single user will be accounted formultiple times when the user has been presented the video more thanonce. For example, a user that is included in the fourth impressiongroup (e.g., the impression group that has seen the video four times),will also be included in the first, second and third impression group.However, variance in the user's viewing session with respect to how longthe user watched the video (e.g., and any other data associated with theuser's viewing session) for each impression of the video will beaccounted for.

Visualization component 108 is configured to generate or configure avisualization for a video based on viewer retention and impressionfrequency information received/generated by reception component 106 thatgraphically depicts viewer retention over duration of the video as afunction of impression frequency. The visualization can berendered/presented at a client device 116 via presentation component118. In an aspect, visualization component 108 can generate a graph thatcharts percentage of users watching a video at respective pointsthroughout the progression of the video. The users can be divided intodifferent audience groups or user buckets, wherein each audience groupor user buck includes users that have been presented the video differentnumbers of times (e.g., first audience group includes users that havebeen presented the video once, second audience group includes users thathave been presented the video twice, etc.). Each of the differentaudience groups or user buckets can be distinguished from one another onthe graph.

Visualization component 108 combines audience retention and audienceimpression frequency data for a video into a single visualization. As aresult, when looking at the visualization, a user can easily discern howviewers drop off from watching the video over the course of the video ingeneral, and how viewers drop off from watching the video over thecourse of the video with respect to different impressions of the video.For example, an advertiser can easily look at a visualization for avideo advertisement generated by visualization component 108 andunderstand that 30% of users who saw the video twice made it to halfwaythrough the video (e.g., 50% of the video was played before the 30% ofusers stopped watching the video) as compared to 60% of users who sawthe video for the first time.

The form and type of visualization generated by visualization component108 can vary. In an aspect, visualization component 108 can generate abar graph or a line graph. However, it should be appreciated that othergraphical depictions that can interpret at least audience retention dataas a function of audience impression frequency for a video areconsidered within the scope of the subject disclosure. For example,other graphical visualizations can include but are not limited to: astacked area graph, a stepped area graph, a pie chart, a histogram, or athree dimensional graph.

FIG. 2 presents an example visualization 200 generated by visualizationcomponent 108 that charts audience retention as a function of impressionfrequency for a video. Visualization 200 is a bar graph for a videoentitled “ABC.” A first axis of the graph corresponds to percent of thevideo watched at each quartile. For example, the horizontal or x-axisincludes sequential markers corresponding to 25% of the video watched,50% of the video watched, 75% of the video watched, and 100% of thevideo watched. It should be appreciated that other metrics can beemployed along the horizontal or x-axis to indicate progression of thevideo (e.g., more granular percentages of the video watched, sequentialsegments of the video watched, sequential points in the video watched,sequential points in the video where viewers dropped off, sequentialpoints in the video where viewers selected an overlay link, etc.).

A second axis of the graph corresponds to percent of viewers. Forexample, the vertical or y-axis includes sequential markerscorresponding to 25% of the viewers, 50% of the viewers, 75% of theviewers, and 100% of the viewers. Sets of bars (e.g., set 202, set 204,set 206 and set 208) are provided at respective markers along thehorizontal axis that extend vertically as a function of the percent ofviewers retained at the respective amounts or percentages of the videowatched. For example, the set of bars located at marker “25% of thevideo watched” extend to points along the vertical axis based onpercentage of viewers that have watched 25% of the video.

In order to distinguish between different impression groups (e.g.,groups of the viewers that have seen the video different number oftimes), each of the sets of bars can include a plurality of sub-barsthat respectively correspond to the different impression groups. Forexample, as identified in legend 210, each set of bars includes sixsub-bars corresponding to six audience impression groups (e.g., firstimpression, second impression, third impression, fourth impression,fifth impression, and sixth impression). It should be appreciated thatthe number of impression groups represented via the graph (andcorresponding sub-bars) can vary. The sub-bars can be distinguished fromone another in appearance. For example, the sub-bars can bedistinguished from one another in color and/or fill pattern.

Each of the six sub-bars respectively included the sets of bars extendvertically from the horizontal axis as a function of the percent ofviewers included in the impression groups represented by the respectivesub-bars, retained at the respective amounts of the percentages of thevideo watched. For example, the sub-bar corresponding to the 1stimpression group located at marker “75% of the video watched” extends topoint the 50% marker for percentage of viewers, indicating that 50% ofviewers make it through 75% of the video upon 1st impression.

FIG. 3 presents another example visualization 300 generated byvisualization component 108 that charts audience retention as a functionof impression frequency for a video. Visualization 300 is a line graphfor a video entitled “EFG.” A first axis of the graph corresponds tosequential time markers in a video. For example, the horizontal orx-axis includes sequential markers corresponding to 0, 5, 10 15, 20, 25,and 30 second time markers in a 30 second video. It should beappreciated that other metrics can be employed along the horizontal orx-axis to indicate progression of the video (e.g., percentages of thevideo watched, sequential segments of the video watched, sequentialpoints in the video watched, sequential points in the video whereviewers dropped off, sequential points in the video where viewersselected an overlay link, etc.).

A second axis of the graph corresponds to percent of viewers retained.For example, the vertical or y-axis includes sequential markerscorresponding to 25% of the viewers, 50% of the viewers, 75% of theviewers, and 100% of the viewers. Lines 302, 304, 306 and 308 aregenerated by visualization component 108 as a function of percentages ofviewers, respectively included in the impression groups represented bythe lines, to remain watching the video at respective sequential timesin the video. In particular, lines 302, 304, 306 and 308 show thedecline in viewer percentage over progression of the video from start tofinish for different impression groups. For example, as identified inlegend 310, line 302 corresponds to a first impression group, line 304corresponds to a second impression group, line 306 corresponds to athird impression group and line 308 corresponds to a fourth impressiongroup. The lines corresponding to the different impression groups can bedifferentiated from one another (e.g., via pattern, texture, color,etc.).

As seen by looking at visualization 300, as impression frequencydecreases, view retention also decreases. For example, when comparingthe first and fourth impression groups, it can be seen that over 75% ofthe viewers watched the video past the 5 second marker when seeing thevideo for the first time while less than 25% of the viewers watched thevideo past the 5 second marker when seeing the video for the fourthtime.

Referring back to FIG. 1, in an aspect, in addition to analyzing andprocessing audience retention data as a function of viewer impressionfrequency, audience review platform 104 is configured to process avariety of other data related to video consumption as a function ofimpression frequency. In particular, in addition to how much of a videoa user watches each subsequent impression to the video, various otherreactions of the user to the video each subsequent impression to thevideo can provide rich information regarding the user and the videoitself. For example, preferences and behaviors of the user can beinferred based on the user's reaction/interaction with a video uponsubsequent impressions. In another example, where a user's skip forwardpast content of a video after a first impression, it can be inferredthat the content of the video skipped over is considered uninterestingby many users. Accordingly, in addition to information relating audienceretention to impression frequency, reception component 106 can receive avariety of other rich information associated with user watch history ofa video that can vary based on impression frequency. Visualizationcomponent 108 can further generate a visualization that graphicallydepicts this other rich information as a function of impressionfrequency.

For example, reception component 106 can receive information related tohow a user interacts with a video with respect to each user impressionof a video and visualization component 108 can generate a visualizationthat demonstrates variance in user interaction with a video based onimpression frequency. For instance, reception component 106 can receiveinformation regarding a user's interaction with the video, including butnot limited to: pausing, playing, rewinding, fast forwarding, seekingforward, or seeking backward. According to this example, visualizationcomponent 108 can generate a graph or chart the depicts how user'sinteract with a video with respect to pausing, playing rewinding, etc.,based on impression frequency.

In another example, reception component 106 can receive informationrelated to hyperlink selection associated with a video with respect toimpression frequency and visualization component 108 can generate avisualization that depicts variance in hyperlink selection based onimpression frequency. For example, video can often include hyperlinks toother sources, such as a hyperlink to a website associated with aproduct advertised in the video, a hyperlink to purchase a productadvertised in the video, a hyperlink to a social networking profileassociated with the video, or a hyperlink to another video or channel, ahyperlink to share the video (e.g., at a social networking source, withanother user vie an electronic message, etc.). According to thisexample, reception component 106 can receive information regarding whathyperlinks a users selects and at what point the user select them inassociation with each viewing/impression of a video. Visualizationcomponent 108 can generate a visualization that depicts percentages ofusers that select a hyperlink, and/or timing of hyperlink selection, asa function of impression frequency. Similarly, visualization component108 can generate a visualization that depicts video advertisementconversion as a function of impression frequency based on informationreceived by reception component 106.

In other aspects, rather than comparing user retention over the courseof a video with impression frequency, visualization component 108 cangenerate a visualization that compares percentages of users to view keyparts of the video or to make it to key time points in the video, as afunction of impression frequency. For example, a video advertisement canbe associated with a time marker, wherein only viewing past the timemarker is considered relevant to the video advertisement (e.g., forbilling purposes or other purposes). A video advertisement can also beassociated with an option to skip or exit the video advertisement afterthe passing of a predetermined minimum amount of time. For example,while viewing a video advertisement, a pop up skip button can bedisplayed that allows the viewer to select the button and exit the videoadvertisement after the passing of a predetermined amount of time (e.g.,5 seconds, 10 seconds, 15 seconds, etc.). According to this example,visualization component 108 can generate a graphical representation(e.g., a histogram) that shows percentages of viewers to make it pastthe key time markers, (e.g., past advertisement skipping point, pastbillable point, etc.) as a function of impression frequency.

For example, FIG. 4 presents another example visualization 400 generatedby visualization component 108 that charts percentages of viewers toview a video advertisement past various key time markers in a video as afunction of impression frequency for the video. Visualization 400 is aline graph for a video advertisement entitled “HU.” In an aspect, videoadvertisement HIJ is played before or during the playing of anotherprimary video. Similar to visualization 300, a first axis of the graphcorresponds to sequential time markers in a video. For example, thehorizontal or x-axis includes sequential markers corresponding to times0:00, 0:06, 0:12, 0:18, etc. A second axis of the graph corresponds topercent of viewers retained. For example, the vertical or y-axisincludes sequential markers corresponding to 25% of the viewers, 50% ofthe viewers, 75% of the viewers, and 100% of the viewers. Lines 402,404, 406 and 408 are generated by visualization component 108 as afunction of percentages of viewers, respectively included in theimpression groups represented by the lines, to remain watching the videoat respective sequential times in the video. In particular, lines 402,404, 406 and 408 show the decline in viewer percentage over progressionof the video from start to finish for different impression groups. Forexample, as identified in legend 410, line 402 corresponds to a firstimpression group, line 404 corresponds to a second impression group,line 406 corresponds to a third impression group and line 408corresponds to a fourth impression group. The lines corresponding to thedifferent impression groups can be differentiated from one another(e.g., via pattern, texture, color, etc.).

In addition, visualization 400 includes vertical markers in the body ofthe graph to indicate time points in the video associated with keypoints. For example, marker 412 indicates the point in the video where abutton to skip the video advertisement is available. Marker 414indicates a point in the video where viewing there past is consideredbillable. As seen by looking at visualization 400, as impressionfrequency increases, the percentage of viewers to not select the skipbutton and to make it past the billable time marker significantlydecreases.

Referring back to FIG. 1, in another aspect, reception component 106 canreceive information regarding different actions a user performs over thecourse of a video and following watching of a video as a function ofimpression frequency. A user can perform various actions in response toa video advertisement while watching the video advertisement and afterwatching the video advertisement. For example, a user can select anoption to skip the advertisement after passage of an amount of time andview a primary video associated with the advertisement. In anotherexample, a user can exit or abandon the primary video and advertisementall together in response to presentation of a video advertisement. Inanother example, a user can watch the advertisement to completion andthen watch the primary video or exit the primary video to view othercontent (e.g., a website associated with the advertisement, a newchannel, a new video, etc.). Still in yet another example, whilewatching or after watching a video, a user can comment on the video,share the video, like the video, etc.

The different actions/paths a user takes in response to viewing a video(such as a video advertisement), can vary based on the differentimpressions of the video. For example, when a user sees a videoadvertisement for the first time, the user may watch it to completionand exit the primary video to view a website associated with the videoadvertisement. However, when the user sees the video for a second time,the user may skip the advertisement and continue to the primary videoassociated with the advertisement after passage of a minimum amount ofrequired advertisement watch time. The manner in which a user reacts todifferent impressions of a video can provide strong insight into whetherrepeated exposure of the user to the video is beneficial, depending onthe type of reaction desired by the video owner in response to therepeated exposure.

Accordingly, visualization component 108 can also be configured togenerate a visualization that graphically depicts different actionsusers performs in response to presentation of a video as a function ofimpression frequency. In an aspect, such a visualization can take theform of a sankey diagram wherein different arrows can extend from pointsin a graph representative of time points in a video where a userperforms a reaction to the video. Different arrows or groups of arrowscan respectively represent a different user action. Further, differentarrows or groups of arrows and can be represented by a different coloror fill pattern to distinguish between different user impression groups.

FIG. 5 presents a diagram of another example system 500 for visualizingaudience retention data as a function of impression frequency, inaccordance with various aspects and embodiments described herein. System500 includes same or similar features and functionalities as system 100with the addition of filter component 502 to audience review platform104. Repetitive description of like elements employed in respectiveembodiments of systems described herein is omitted for sake of brevity.

Filter component 502 is configured to facilitate generation of differentversions or pivots of visualizations generated by visualizationcomponent 108 based on various characteristics associated audiencesubsets. In particular, rather than generating a visualization depictingrelationships between aspects associated with video consumption (e.g.,viewer retention, hyperlink selection, etc.) that vary based on videoimpression frequency for all audience members or viewers, filtercomponent 502 can identify a subset of audience members to represent ina visualization. Accordingly, visualization component 108 can generatemultiple different visualizations for different subsets of audiences.These multiple different visualizations can be compared to one anotherto assist in analyzing and interpreting differences in audience groupswith respect to video consumption based on impression frequency. Thesubset of audience members or viewers can be filtered based on variousfactors including by not limited to: a demographic characteristic (e.g.,age, gender, ethnicity, occupation, marital status, language, etc.), auser preference, user watch history, a user interest, a socialaffiliation, a user context, or a user location.

For example, FIG. 6 illustrates example visualizations 600 and 601comparing audience retention per impression for video “ABC” whenpresented to a male audience with audience retention per impression forvideo “ABC” when presented to a female audience. Visualizations 600 and601 are filtered versions of visualization 200. As can be seen bycomparing visualizations 600 and 601, various difference are presentbetween the manner in which female audience members watch the video uponsubsequent impressions compared to that of the males. For example,audience retention for the male audience is substantially higher thanaudience retention for the female audience up to the 50% point in theamount of video watched with respect to the 2nd, 3rd, 4th, 5th and 6thimpression groups.

In an aspect, rather than depicting different filtered audience groupsin different graphs, a visualization component 108 can generate a singlegraph that identifies different audience groups as a function ofimpression frequency and another characteristic (e.g., age, gender,location, preference, etc.).

FIG. 7 presents a diagram of another example system 700 for visualizingaudience retention data as a function of impression frequency, inaccordance with various aspects and embodiments described herein. System700 includes same or similar features and functionalities as system 500with the addition of input component 702 to audience review platform104. Repetitive description of like elements employed in respectiveembodiments of systems described herein is omitted for sake of brevity.

Input component 702 is configured to receive user input regardingdesired features of a visualization so that visualization component 108can generate custom visualizations for users based on their needs. Forexample, input component 702 can configure a user interface forgeneration at client device 116 (e.g., via presentation component 118)that allows a user to select characteristics of a visualization forgeneration by visualization component 108. For example, via the userinterface, input component 702 can receive selection of a video and arequest for a visualization of that video related to how the video hasbeen consumed by viewers with respect to various comparison metrics. Forexample, input component 702 can receive a request for a visualizationthat graphically depicts viewer retention for the video as a function ofimpression frequency. In response to the request, visualizationcomponent 108 can generate an appropriate visualization as describedherein (e.g., visualizations 200, 300, 400, 600, 601 and the like).

In an aspect, in association with the request for the visualization thatgraphically depicts viewer retention for the video as a function ofimpression frequency, input component 702 can receive selection of atype of visualization desired (e.g., a bar graph, a line graph, a threedimension graph, etc.). In addition, input component 702 can receiveinput regarding the number of impression groups desired for inclusion inthe visualization. Input component 702 can also receive user inputregarding the axes of the graph with respect to correspondence (e.g.,what the respective axes represent) and granularity.

Input component 702 can also receive user selection of other types ofcomparison visualizations between user video consumption data based onimpression frequency. For example, rather than receiving a request for avisualization that graphically depicts viewer retention for a video as afunction of impression frequency, input component 702 can receive arequest for a visualization that graphically depicts video interaction(e.g., pausing, fast forwarding, rewinding, etc.) for the video as afunction of impression frequency. In another example, input component702 can receive a request for a visualization that graphically depictshyperlink selection for the video as a function of impression frequency.In yet another example, input component 702 can receive a request for avisualization that graphically depicts video advertisement conversion asa function of impression frequency.

In addition, input component 702 can receive selection of variousfilters to apply to a visualization regarding audience subtypesrepresented in by a visualization. For example, input component 702 canreceive selection of one or more filters that restrict audience membersbased on a demographic characteristic, a location, a context, a socialaffiliation, a user interest etc.

FIG. 8 depicts an exemplary user interface 800 that facilitatesreviewing audience watch history information in accordance with aspectsand embodiments described herein. In particular, interface 800 depicts auser account or profile for user “Erin” that facilitates reviewing watchhistory information regarding user consumption of videos that Erin hasbeen granted authority to review the watch history information thereof.For example, the left side of the interface includes various menuoptions that organize access to information regarding Erin's profile,information regarding videos associated with Erin (e.g., videos createdand/or controlled by Erin on her channel), settings, and audience reviewinformation 802. The audience review information 802 tab can allow Erinto review information regarding how her video has been viewed/watchedand otherwise consumed by users. In an aspect, the audience reviewinformation tab 802 can include various sub-menu options, including anoption to review user watch history data, an option to generate avisualization 804 to facilitate analyzing watch history data, and anoption to view optimization suggestions 806 associated with improvingaspects associated with consumption of her videos based on automatedanalysis of a generated visualization (e.g., discussed infra withrespect to FIG. 9).

In an aspect, selection of the visualization sub-menu option 804 resultsin the generation of sections 808 and 810. Section 810 provides aninteractive input menu that allows Erin to input requirements (e.g., viaselection boxes and/or drop down menus) for a custom visualization thatfacilitates analysis of the watch history information for a selectedvideo. For example, section 810 includes options for selection of avideo for which to generate a visualization, selection of a graph type,selection of a number of impression frequencies to consider, selectionof another audience review metric for which to analyze the impressionfrequencies and selection of one or more audience filters (e.g., age,gender, ethnicity, preference, location, social group, and context).Section 808 can display a generated visualization based on the criteriaselected in section 810. In an aspect, as a user changes selectioncriteria in section 810, the visualization provided in section 708 isdynamically updated.

FIG. 9 presents a diagram of another example system 900 for visualizingaudience retention data as a function of impression frequency, inaccordance with various aspects and embodiments described herein. System900 includes same or similar features and functionalities as system 700with the addition of optimization component 902 and inference component904 to audience review platform 104. Repetitive description of likeelements employed in respective embodiments of systems described hereinis omitted for sake of brevity.

Optimization component 902 is configured to provide suggested changesassociated with placement of a video and creation of new videos based onanalysis of visualizations and associated data relating videoconsumption and impression frequency. In an aspect, optimizationcomponent 902 is configured to analyze a visualization generated byvisualization component 108 and infer placement tactics for a video toincrease total audience retention with respect to frequency at which thevideo is viewed. The placement tactics can account for audience typesand in the case of video advertisement, channels and or other videos inwhich the video advertisements are associated (e.g., as a pre-rolladvertisement, in-video advertisement, post-video advertisement, etc.).Optimization component 902 can provide the placement tactics to an owneror manager (e.g., or entity otherwise in control of the placement of thevideo) of the video to facilitate advising the owner regarding how tochose where to place the video (e.g., where the video is anadvertisement, what users to target with the video advertisement). Forexample, placement tactics outlining promising audiences to target forrepeat exposure, maximum repeat exposures, context for repeat exposures,etc. of a video advertisement can influence video advertisers whenbidding for advertisement placement opportunities as well as creatingnew and more enticing videos.

In an aspect, optimization component 902 can infer or determine amaximum impression threshold for which to present the video to users.According to this example, after a user has been presented a video anumber of times equal the threshold, the user will no longer bepresented the video. Optimization component 902 can also tailorimpression placement thresholds based on user groups or subsets (e.g.,threshold is based on a characteristic of a user subset, such as age,gender interest, etc.) as well as individual users (e.g., threshold isset for user John Doe based on his previous watch history).

In another aspect, optimization component 902 can identify differentuser groups to target for repeat exposure to a video based onvisualization analysis. For example, with reference to FIG. 6, based oncomparison of visualizations 600 and 601 representing the male andfemale audiences, optimization component 902 can determine or infer thatrepeat exposure of video ABC to the male audience at least up to threetimes is more beneficial than repeat exposure to the female audience.Optimization component 902 can also infer or determine impressionthresholds and targeted audience groups for repeat exposure based onanalysis of hyperlink selection with respect to impression frequency inorder to increase overall hyperlink selection (particularly with respectto advertisement conversion).

Inference component 904 is configured to provide for or aid in variousinferences or determinations associated with aspects of audience reviewplatform 104. For example, inference component 904 can facilitateoptimization component 902 with inferring placement tactics for a videoto increase total audience retention with respect to frequency at whichthe video is viewed. In aspect, all or portions of media provider 102can be operatively coupled to inference component 904. Moreover,inference component 904 can be granted access to all or portions ofremote content sources, external information sources and client devices(e.g., client device 116).

In order to provide for or aid in the numerous inferences describedherein, inference component 904 can examine the entirety or a subset ofthe data to which it is granted access and can provide for reasoningabout or infer states of the system, environment, etc. from a set ofobservations as captured via events and/or data. An inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states, for example. The inference can beprobabilistic—that is, the computation of a probability distributionover states of interest based on a consideration of data and events. Aninference can also refer to techniques employed for composinghigher-level events from a set of events and/or data.

Such an inference can result in the construction of new events oractions from a set of observed events and/or stored event data, whetheror not the events are correlated in close temporal proximity, andwhether the events and data come from one or several event and datasources. Various classification (explicitly and/or implicitly trained)schemes and/or systems (e.g., support vector machines, neural networks,expert systems, Bayesian belief networks, fuzzy logic, data fusionengines, etc.) can be employed in connection with performing automaticand/or inferred action in connection with the claimed subject matter.

A classifier can map an input attribute vector, x=(x1, x2, x3, x4, xn),to a confidence that the input belongs to a class, such as byf(x)=confidence(class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to prognose or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hyper-surface in the space of possible inputs, where thehyper-surface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachesinclude, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

In view of the example systems and/or devices described herein, examplemethods that can be implemented in accordance with the disclosed subjectmatter can be further appreciated with reference to flowcharts in FIGS.10-11. For purposes of simplicity of explanation, example methodsdisclosed herein are presented and described as a series of acts;however, it is to be understood and appreciated that the disclosedsubject matter is not limited by the order of acts, as some acts mayoccur in different orders and/or concurrently with other acts from thatshown and described herein. For example, a method disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methods. Furthermore,not all illustrated acts may be required to implement a method inaccordance with the subject specification. It should be furtherappreciated that the methods disclosed throughout the subjectspecification are capable of being stored on an article of manufactureto facilitate transporting and transferring such methods to computersfor execution by a processor or for storage in a memory.

FIG. 10 illustrates a flow chart of an example method 1000 forvisualizing video audience retention as a function of impressionfrequency, in accordance with various aspects and embodiments describedherein. At 1002, watch history information for a plurality of usersregarding their watch history of a video is received (e.g., viareception component 106). At 1004, a visualization is generated based onthe watch history information that graphically depicts viewer retentionover duration of the video as a function of impression frequency (e.g.,via visualization component 108). As previously described, viewerretention over the duration of the video refers to percentages of theplurality of users that have viewed the video up to respective points inthe video, and impression frequency relates to number of times the videohas been presented to respective users of the plurality of users.

In an aspect the visualization accounts for impression frequency bydefining a plurality of viewer buckets, wherein each bucket includeswatch history data for different user impressions of the video. Forexample, a first impression bucket will include watch history data basedon users' first times seeing the video, a second impression bucket willinclude watch history data based on users' second times seeing thevideo, etc. A single viewer that has seen the video more than once willbe accounted for in each impression bucket corresponding to the numberof times the viewer has seen the video. For example, watch history datafor a user's first impression of the video will be accounted for in thefirst impression bucket, watch history data for the same user's secondimpression of the video will be accounted for in the second impressionbucket, etc.

FIG. 11 illustrates a flow chart of another example method 1100 forvisualizing video audience retention as a function of impressionfrequency, in accordance with various aspects and embodiments describedherein. At 1102, watch history information for a plurality of usersregarding their watch history of a video is received (e.g., viareception component 106). At 1104, a visualization is generated based onthe watch history information that graphically depicts viewer retentionover duration of the video as a function of impression frequency (e.g.,via visualization component 108). At 1106, the plurality of users arefiltered into a subset of the plurality of users based on a demographiccharacteristic associated with the subset of the plurality of users(e.g., via filter component 502). For example, the plurality of userscan be filtered based on age, gender, user preference, affiliation witha same social circle, affiliation with a same social network, location,language, context, etc. At 1108, another visualization is generated(e.g., via visualization component 108) that compares the viewerretention over the duration of the video for different groups of thesubset of the plurality of users, wherein the different groups of thesubset of the plurality of users are defined based on number of timesrespective users of the different groups were presented the video.

EXAMPLE OPERATING ENVIRONMENTS

The systems and processes described below can be embodied withinhardware, such as a single integrated circuit (IC) chip, multiple ICs,an application specific integrated circuit (ASIC), or the like. Further,the order in which some or all of the process blocks appear in eachprocess should not be deemed limiting. Rather, it should be understoodthat some of the process blocks can be executed in a variety of orders,not all of which may be explicitly illustrated in this disclosure.

With reference to FIG. 12, a suitable environment 1200 for implementingvarious aspects of the claimed subject matter includes a computer 1202.The computer 1202 includes a processing unit 1204, a system memory 1206,a codec 1235, and a system bus 1208. The system bus 1208 couples systemcomponents including, but not limited to, the system memory 1206 to theprocessing unit 1204. The processing unit 1204 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as the processing unit 1204.

The system bus 1208 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 13124), and SmallComputer Systems Interface (SCSI).

The system memory 1206 includes volatile memory 1210 and non-volatilememory 1212. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1202, such as during start-up, is stored in non-volatile memory 1212. Inaddition, according to present innovations, codec 1235 may include atleast one of an encoder or decoder, wherein the at least one of anencoder or decoder may consist of hardware, a combination of hardwareand software, or software. Although, codec 1235 is depicted as aseparate component, codec 1235 may be contained within non-volatilememory 1212. By way of illustration, and not limitation, non-volatilememory 1212 can include read only memory (ROM), programmable ROM (PROM),electrically programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), or flash memory. Volatile memory 1210includes random access memory (RAM), which acts as external cachememory. According to present aspects, the volatile memory may store thewrite operation retry logic (not shown in FIG. 12) and the like. By wayof illustration and not limitation, RAM is available in many forms suchas static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM),double data rate SDRAM (DDR SDRAM), and enhanced SDRAM (ESDRAM.

Computer 1202 may also include removable/non-removable,volatile/non-volatile computer storage medium. FIG. 12 illustrates, forexample, disk storage 1214. Disk storage 1214 includes, but is notlimited to, devices like a magnetic disk drive, solid state disk (SSD)floppy disk drive, tape drive, Jaz drive, Zip drive, LS-70 drive, flashmemory card, or memory stick. In addition, disk storage 1214 can includestorage medium separately or in combination with other storage mediumincluding, but not limited to, an optical disk drive such as a compactdisk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage devices 1214 tothe system bus 1208, a removable or non-removable interface is typicallyused, such as interface 1216.

It is to be appreciated that FIG. 12 describes software that acts as anintermediary between users and the basic computer resources described inthe suitable operating environment 1200. Such software includes anoperating system 1218. Operating system 1218, which can be stored ondisk storage 1214, acts to control and allocate resources of thecomputer system 1202. Applications 1220 take advantage of the managementof resources by operating system 1218 through program modules 1224, andprogram data 1226, such as the boot/shutdown transaction table and thelike, stored either in system memory 1206 or on disk storage 1214. It isto be appreciated that the claimed subject matter can be implementedwith various operating systems or combinations of operating systems.

A user enters commands or information into the computer 1202 throughinput device(s) 1228. Input devices 1228 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1204through the system bus 1208 via interface port(s) 1230. Interfaceport(s) 1230 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1236 usesome of the same type of ports as input device(s). Thus, for example, aUSB port may be used to provide input to computer 1202, and to outputinformation from computer 1202 to an output device 1236. Output adapter1234 is provided to illustrate that there are some output devices 1236like monitors, speakers, and printers, among other output devices 1236,which require special adapters. The output adapters 1234 include, by wayof illustration and not limitation, video and sound cards that provide ameans of connection between the output device 1236 and the system bus1208. It should be noted that other devices and/or systems of devicesprovide both input and output capabilities such as remote computer(s)1238.

Computer 1202 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1238. The remote computer(s) 1238 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device, a smart phone, a tablet, or other network node, andtypically includes many of the elements described relative to computer1202. For purposes of brevity, only a memory storage device 1240 isillustrated with remote computer(s) 1238. Remote computer(s) 1238 islogically connected to computer 1202 through a network interface 1242and then connected via communication connection(s) 1244. Networkinterface 1242 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN) and wide-area networks (WAN) andcellular networks. LAN technologies include Fiber Distributed DataInterface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet,Token Ring and the like. WAN technologies include, but are not limitedto, point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1244 refers to the hardware/softwareemployed to connect the network interface 1242 to the bus 1208. Whilecommunication connection 1244 is shown for illustrative clarity insidecomputer 1202, it can also be external to computer 1202. Thehardware/software necessary for connection to the network interface 1242includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and wired and wirelessEthernet cards, hubs, and routers.

Referring now to FIG. 13, there is illustrated a schematic block diagramof a computing environment 1300 in accordance with this disclosure. Thesystem 1300 includes one or more client(s) 1302 (e.g., laptops, smartphones, PDAs, media players, computers, portable electronic devices,tablets, and the like). The client(s) 1302 can be hardware and/orsoftware (e.g., threads, processes, computing devices). The system 1300also includes one or more server(s) 1304. The server(s) 1304 can also behardware or hardware in combination with software (e.g., threads,processes, computing devices). The servers 1304 can house threads toperform transformations by employing aspects of this disclosure, forexample. One possible communication between a client 1302 and a server1304 can be in the form of a data packet transmitted between two or morecomputer processes wherein the data packet may include video data. Thedata packet can include a metadata, e.g., associated contextualinformation, for example. The system 1300 includes a communicationframework 1306 (e.g., a global communication network such as theInternet, or mobile network(s)) that can be employed to facilitatecommunications between the client(s) 1302 and the server(s) 1304.

Communications can be facilitated via a wired (including optical fiber)and/or wireless technology. The client(s) 1302 include or areoperatively connected to one or more client data store(s) 1308 that canbe employed to store information local to the client(s) 1302 (e.g.,associated contextual information). Similarly, the server(s) 1304 areoperatively include or are operatively connected to one or more serverdata store(s) 1310 that can be employed to store information local tothe servers 1304.

In one embodiment, a client 1302 can transfer an encoded file, inaccordance with the disclosed subject matter, to server 1304. Server1304 can store the file, decode the file, or transmit the file toanother client 1302. It is to be appreciated, that a client 1302 canalso transfer uncompressed file to a server 1304 and server 1304 cancompress the file in accordance with the disclosed subject matter.Likewise, server 1304 can encode video information and transmit theinformation via communication framework 1306 to one or more clients1302.

The illustrated aspects of the disclosure may also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Moreover, it is to be appreciated that various components described inthis description can include electrical circuit(s) that can includecomponents and circuitry elements of suitable value in order toimplement the embodiments of the subject innovation(s). Furthermore, itcan be appreciated that many of the various components can beimplemented on one or more integrated circuit (IC) chips. For example,in one embodiment, a set of components can be implemented in a single ICchip. In other embodiments, one or more of respective components arefabricated or implemented on separate IC chips.

What has been described above includes examples of the embodiments ofthe present invention. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the claimed subject matter, but it is to be appreciated thatmany further combinations and permutations of the subject innovation arepossible. Accordingly, the claimed subject matter is intended to embraceall such alterations, modifications, and variations that fall within thespirit and scope of the appended claims. Moreover, the above descriptionof illustrated embodiments of the subject disclosure, including what isdescribed in the Abstract, is not intended to be exhaustive or to limitthe disclosed embodiments to the precise forms disclosed. While specificembodiments and examples are described in this disclosure forillustrative purposes, various modifications are possible that areconsidered within the scope of such embodiments and examples, as thoseskilled in the relevant art can recognize.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms used to describe such components are intended to correspond,unless otherwise indicated, to any component which performs thespecified function of the described component (e.g., a functionalequivalent), even though not structurally equivalent to the disclosedstructure, which performs the function in the disclosure illustratedexemplary aspects of the claimed subject matter. In this regard, it willalso be recognized that the innovation includes a system as well as acomputer-readable storage medium having computer-executable instructionsfor performing the acts and/or events of the various methods of theclaimed subject matter.

The aforementioned systems/circuits/modules have been described withrespect to interaction between several components/blocks. It can beappreciated that such systems/circuits and components/blocks can includethose components or specified sub-components, some of the specifiedcomponents or sub-components, and/or additional components, andaccording to various permutations and combinations of the foregoing.Sub-components can also be implemented as components communicativelycoupled to other components rather than included within parentcomponents (hierarchical). Additionally, it should be noted that one ormore components may be combined into a single component providingaggregate functionality or divided into several separate sub-components,and any one or more middle layers, such as a management layer, may beprovided to communicatively couple to such sub-components in order toprovide integrated functionality. Any components described in thisdisclosure may also interact with one or more other components notspecifically described in this disclosure but known by those of skill inthe art.

In addition, while a particular feature of the subject innovation mayhave been disclosed with respect to only one of several implementations,such feature may be combined with one or more other features of theother implementations as may be desired and advantageous for any givenor particular application. Furthermore, to the extent that the terms“includes,” “including,” “has,” “contains,” variants thereof, and othersimilar words are used in either the detailed description or the claims,these terms are intended to be inclusive in a manner similar to the term“comprising” as an open transition word without precluding anyadditional or other elements.

As used in this application, the terms “component,” “module,” “system,”or the like are generally intended to refer to a computer-relatedentity, either hardware (e.g., a circuit), a combination of hardware andsoftware, software, or an entity related to an operational machine withone or more specific functionalities. For example, a component may be,but is not limited to being, a process running on a processor (e.g.,digital signal processor), a processor, an object, an executable, athread of execution, a program, and/or a computer. By way ofillustration, both an application running on a controller and thecontroller can be a component. One or more components may reside withina process and/or thread of execution and a component may be localized onone computer and/or distributed between two or more computers. Further,a “device” can come in the form of specially designed hardware;generalized hardware made specialized by the execution of softwarethereon that enables the hardware to perform specific function; softwarestored on a computer readable storage medium; software transmitted on acomputer readable transmission medium; or a combination thereof.

Moreover, the words “example” or “exemplary” are used in this disclosureto mean serving as an example, instance, or illustration. Any aspect ordesign described in this disclosure as “exemplary” is not necessarily tobe construed as preferred or advantageous over other aspects or designs.Rather, use of the words “example” or “exemplary” is intended to presentconcepts in a concrete fashion. As used in this application, the term“or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise, or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Computing devices typically include a variety of media, which caninclude computer-readable storage media and/or communications media, inwhich these two terms are used in this description differently from oneanother as follows. Computer-readable storage media can be any availablestorage media that can be accessed by the computer, is typically of anon-transitory nature, and can include both volatile and nonvolatilemedia, removable and non-removable media. By way of example, and notlimitation, computer-readable storage media can be implemented inconnection with any method or technology for storage of information suchas computer-readable instructions, program modules, structured data, orunstructured data. Computer-readable storage media can include, but arenot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

On the other hand, communications media typically embodycomputer-readable instructions, data structures, program modules orother structured or unstructured data in a data signal that can betransitory such as a modulated data signal, e.g., a carrier wave orother transport mechanism, and includes any information delivery ortransport media. The term “modulated data signal” or signals refers to asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in one or more signals. By way ofexample, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

In view of the exemplary systems described above, methodologies that maybe implemented in accordance with the described subject matter will bebetter appreciated with reference to the flowcharts of the variousfigures. For simplicity of explanation, the methodologies are depictedand described as a series of acts. However, acts in accordance with thisdisclosure can occur in various orders and/or concurrently, and withother acts not presented and described in this disclosure. Furthermore,not all illustrated acts may be required to implement the methodologiesin accordance with certain aspects of this disclosure. In addition,those skilled in the art will understand and appreciate that themethodologies could alternatively be represented as a series ofinterrelated states via a state diagram or events. Additionally, itshould be appreciated that the methodologies disclosed in thisdisclosure are capable of being stored on an article of manufacture tofacilitate transporting and transferring such methodologies to computingdevices. The term article of manufacture, as used in this disclosure, isintended to encompass a computer program accessible from anycomputer-readable device or storage media.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes at leastthe following computer executable components stored in the memory: areception component configured to receive watch history information fora plurality of viewers regarding their watch history of a video; and avisualization component configured to generate a visualization, on adisplay device, based on the watch history information that graphicallydepicts viewer retention over duration of the video as a function ofimpression frequency, wherein the visualization graphically depictsrespective groups of a plurality of groups of viewers as distinctelements over the duration of the video, each group represents adistinct impression frequency number from 1 to N, where N is an integergreater than one, and an impression frequency number indicates arepetition number of presentation of the video to a same viewer; and anoptimization component configured to determine maximum impressionthresholds for the video based upon an analysis of the viewer retentionover the duration of the video as the function of the impressionfrequency, wherein a maximum impression threshold defines a maximumrepetition for presentation of the video to a viewer, and wherein amaximum impression threshold is determined for at least two differentuser groups based on respective characteristics of the user groups, thecharacteristics including at least one of ages associated with the usergroups or interests of the user groups.
 2. The system of claim 1,wherein viewer retention over the duration of the video indicatesnumbers of the plurality of viewers viewing the video at respectivepoints in the video, and wherein impression frequency relates to numbersof times the video has been presented to respective viewers of theplurality of viewers.
 3. The system of claim 1, further comprising afilter component configured to filter the plurality of viewers into asubset of the plurality of viewers based on a demographic characteristicassociated with the subset of the plurality of viewers, and wherein thevisualization component is configured to generate another visualizationto reflect an updated version of the visualization restricted to watchhistory information based on the subset of the plurality of viewers. 4.The system of claim 3, further comprising: an input component configuredto receive input requesting the other visualization and identifying thedemographic characteristic, wherein the visualization component isconfigured to generate the other visualization in response to the input.5. The system of claim 1, wherein the visualization comprises a graphcomprising: a first axis representing distinct incremental amounts ofthe video watched; and a second axis, perpendicular to the first axis,representing distinct incremental amounts of viewers that viewed thevideo.
 6. The system of claim 5, wherein the distinct elements comprisesets of bars that respectively extend from the first axis as a functionof the second axis, wherein the sets of bars respectively comprise aplurality of sub-bars, and each sub-bar corresponds to a group of theplurality of groups.
 7. The system of claim 6, wherein the visualizationdifferentiates the plurality of sub-bars from one another in appearance.8. The system of claim 5, wherein the distinct elements comprise aplurality of lines respectively formed via points plotted as a functionof the first axis and the second axis based on the viewer retention overthe duration of the video as a function of impression frequency, whereineach line represents a group of the plurality of groups.
 9. The systemof claim 1, further comprising a presentation component configured todisplay the visualization via a graphical user interface (GUI) whereinthe GUI is adaptively configured based at least in part on displaycharacteristics of a rendering device.
 10. The system of claim 1,wherein the optimization component is further configured to determinethe at least one maximum impression threshold for the video based uponan analysis of hyperlink selection with respect to the impressionfrequency.
 11. A method comprising: receiving, by a system including aprocessor, watch history information for a plurality of users regardingtheir watch history of a video; generating, by the system on a displaydevice, a visualization based on the watch history information thatgraphically depicts user retention over duration of the video as afunction of impression frequency, wherein the visualization graphicallydepicts respective groups of a plurality of groups of users as distinctelements over the duration of the video, each group represents adistinct impression frequency number from 1 to N, where N is an integergreater than one, and an impression frequency number indicates arepetition number of viewing of the video by a same user; anddetermining, by the system, maximum impression thresholds for the videobased upon an analysis of the user retention over the duration of thevideo as the function of the impression frequency, wherein a maximumimpression threshold defines a maximum repetition for presentation ofthe video to a user, and wherein a maximum impression threshold isdetermined for at least two different user groups based on respectivecharacteristics of the user groups, the characteristics including atleast one of ages associated with the user groups or interests of theuser groups.
 12. The method of claim 11, wherein user retention over theduration of the video indicates numbers of the plurality of usersviewing the video at respective points in the video, and whereinimpression frequency relates to numbers of times the video has beenpresented to respective users of the plurality of users.
 13. The methodof claim 11, further comprising: filtering, by the system, the pluralityof users into a subset of the plurality of users based on a demographiccharacteristic associated with the subset of the plurality of users; andgenerating, by the system, another visualization to reflect an updatedversion of the visualization restricted to watch history informationbased on the subset of the plurality of users.
 14. The method of claim13, further comprising: receiving, by the system, input requesting theother visualization and identifying the demographic characteristic,wherein the generating the other visualization is responsive to theinput.
 15. The method of claim 11, wherein the visualization comprises agraph comprising: a first axis corresponding to distinct incrementalamounts of the video watched; and a second axis, perpendicular to thefirst axis, corresponding to distinct incremental amounts of viewersthat viewed the video.
 16. The method of claim 15, wherein the distinctelements comprise sets of bars that respectively extend from the firstaxis as a function of the second axis, wherein the sets of barsrespectively comprise a plurality of sub-bars, and each sub-barcorresponds to a group of the plurality of groups.
 17. The method ofclaim 15, wherein the distinct elements comprise a plurality of linesrespectively formed via points plotted as a function of the first axisand the second axis based on the user retention over the duration of thevideo as a function of impression frequency, wherein each linerepresents a group of the plurality of groups.
 18. A non-transitorycomputer-readable medium having instructions stored thereon that, inresponse to execution, cause a system including a processor to performoperations comprising: receiving information for a plurality of viewersregarding their watch history of a video; generating a visualization, ona display device, based on the information that graphically depictsviewer retention over duration of the video as a function of impressionfrequency, wherein the visualization graphically depicts respectivegroups of a plurality of groups of viewers as distinct elements over theduration of the video, each group represents a distinct impressionfrequency number from 1 to N, where N is an integer greater than one,and an impression frequency number indicates a repetition number ofpresentation of the video to a same viewer; and determining maximumimpression thresholds for the video based upon an analysis of the viewerretention over the duration of the video as the function of theimpression frequency, wherein a maximum impression threshold defines amaximum repetition for presentation of the video to a viewer, andwherein a maximum impression threshold is determined for at least twodifferent user groups based on respective characteristics of the usergroups, the characteristics including at least one of ages associatedwith the user groups or interests of the user groups.